lowbirthweight <- read_csv("csv_NYC_lowbirthweight.csv")
## Rows: 87 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Region/County, 2018, 2019, 2020, Total
## dbl (1): Percentage
## num (1): Average births
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pm2_5 <- read_csv("pm2.5.csv")
## New names:
## Rows: 62 Columns: 8
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): State, County, Data Comment dbl (4): StateFIPS, CountyFIPS, Year, Value
## lgl (1): ...8
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...8`
edu_NY <- read_excel("Edu_NY.xlsx")
race_NY <- read_excel("Race_NY.xlsx")
## New names:
## • `` -> `...1`
HHincome_NY <- read_excel("HHincome_NY.xlsx")
## New names:
## • `` -> `...1`
Age_NY <- read_excel("Age_NY.xlsx")
## New names:
## • `` -> `...1`
Sex_NY <- read_excel("Sex_NY.xlsx")
## New names:
## • `` -> `...1`
health <- read_excel("chir_current_data.xlsx")
uscounties <- read_csv("uscounties.csv") #Simplemaps.com
## Rows: 3143 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): county, county_ascii, county_full, county_fips, state_id, state_name
## dbl (3): lat, lng, population
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pm2_5 <- pm2_5 %>%
janitor::clean_names()%>%
select (county,value) %>%
rename (annual_pm2.5 = "value")
The dataset is obtained from EPH Tracking Website from CDC (https://ephtracking.cdc.gov/DataExplorer/). This has 62
rows and 2 columns of data. In which, 2 variables are:
- county: NY county name
- annual_pm2.5: annual estimated PM2.5 concentration at
each county (ug/m^3)
The following set of demographic datasets is obtained from Census Reporter webpage (https://censusreporter.org/).
edu <- edu_NY %>%
janitor::clean_names()%>%
mutate (percentage_high_education = (bach_male+master_male+prof_male+doct_male+bach_female+master_female+prof_female+doct_female)/total) %>%
filter(str_detect (name, " County, NY")) %>%
mutate(county = str_replace (name, " County, NY", "")) %>%
select(county,percentage_high_education) %>%
mutate(county = str_replace (county, "St.", "St")) %>%
mutate(county = str_replace (county, "Stuben", "Steuben"))%>%
select(county,percentage_high_education)
This has 62 rows and 2 columns of data. In which, 2 variables
are:
- county: NY county name
- percentage_high_education: percentage of population who
finished a higher education level (higher than a bachelor degree)
race_non_hisp_white <- race_NY %>%
janitor::clean_names()%>%
filter( x1 == "White Non-Hispanic") %>%
pivot_longer(
albany_county_ny : yates_county_ny,
names_to = "county",
values_to = "percent_non_hisp_white") %>%
select (county,percent_non_hisp_white) %>%
separate(county, into = c("county", "x"), sep = "_county_") %>%
select (county,percent_non_hisp_white ) %>%
mutate(county = str_replace (county, "_", " ")) %>%
mutate_at(vars(county), str_to_title)
race_non_hisp_black <- race_NY %>%
janitor::clean_names()%>%
filter( x1 == "Black Non-Hispanic") %>%
pivot_longer(
albany_county_ny : yates_county_ny,
names_to = "county",
values_to = "percent_non_hisp_black") %>%
select (county,percent_non_hisp_black) %>%
separate(county, into = c("county", "x"), sep = "_county_") %>%
select (county,percent_non_hisp_black ) %>%
mutate(county = str_replace (county, "_", " ")) %>%
mutate_at(vars(county), str_to_title)
race_hisp_white <- race_NY %>%
janitor::clean_names()%>%
filter( x1 == "White Hispanic") %>%
pivot_longer(
albany_county_ny : yates_county_ny,
names_to = "county",
values_to = "percent_hisp_white") %>%
select (county,percent_hisp_white) %>%
separate(county, into = c("county", "x"), sep = "_county_") %>%
select (county,percent_hisp_white ) %>%
mutate(county = str_replace (county, "_", " ")) %>%
mutate_at(vars(county), str_to_title)
race_hisp_black <- race_NY %>%
janitor::clean_names()%>%
filter( x1 == "Black Hispanic") %>%
pivot_longer(
albany_county_ny : yates_county_ny,
names_to = "county",
values_to = "percent_hisp_black") %>%
select (county,percent_hisp_black) %>%
separate(county, into = c("county", "x"), sep = "_county_") %>%
select (county,percent_hisp_black ) %>%
mutate(county = str_replace (county, "_", " ")) %>%
mutate_at(vars(county), str_to_title)
race_merge <- race_non_hisp_white %>%
inner_join(race_non_hisp_black, by = "county") %>%
inner_join(race_hisp_white, by = "county") %>%
inner_join(race_hisp_black, by = "county")
race_merge <- race_merge %>%
mutate (percent_other = 1 - percent_non_hisp_white - percent_non_hisp_black - percent_hisp_white - percent_hisp_black)
This has 62 rows and 6 columns of data. In which, 6 variables
are:
- county: NY county name
- percent_non_hisp_white: percentage of population who are
identified as Non-Hispanic White
- percent_non_hisp_black: percentage of population who are
identified as Non-Hispanic Black
- percent_hisp_white: percentage of population who are
identified as Hispanic White
- percent_hisp_black: percentage of population who are
identified as Hispanic Black
- percent_other: percentage of population who are
identified as any other ethinic groups
income <- HHincome_NY %>%
janitor::clean_names() %>%
filter (x1 == "percent_high_income") %>%
pivot_longer(
albany_county_ny : yates_county_ny,
names_to = "county",
values_to = "percent_high_income") %>%
select (county,percent_high_income) %>%
separate(county, into = c("county", "x"), sep = "_county_") %>%
select (county,percent_high_income ) %>%
mutate(county = str_replace (county, "_", " ")) %>%
mutate_at(vars(county), str_to_title)
This has 62 rows and 2 columns of data. In which, 2 variables
are:
- county: NY county name
- percent_high_income: percentage of population who are at
higher income households (>$75,000 annually)
age <- Age_NY %>%
janitor::clean_names() %>%
pivot_longer(
albany_county_ny : yates_county_ny,
names_to = "county",
values_to = "median_age") %>%
select (county,median_age) %>%
separate(county, into = c("county", "x"), sep = "_county_") %>%
select (county,median_age ) %>%
mutate(county = str_replace (county, "_", " ")) %>%
mutate_at(vars(county), str_to_title)
This has 62 rows and 2 columns of data. In which, 2 variables
are:
- county: NY county name
- median_age: median age of the population at each
county
sex <- Sex_NY %>%
janitor::clean_names() %>%
filter (x1 == "Male:") %>%
pivot_longer(
albany_county_ny : yates_county_ny,
names_to = "county",
values_to = "percent_male") %>%
select (county,percent_male) %>%
separate(county, into = c("county", "x"), sep = "_county_") %>%
select (county,percent_male ) %>%
mutate(county = str_replace (county, "_", " ")) %>%
mutate_at(vars(county), str_to_title)
This has 62 rows and 2 columns of data. In which, 2 variables
are:
- county: NY county name
- percent_male: percentage of population who are identified
as male
lowbirthweight <- lowbirthweight %>%
janitor::clean_names()%>%
select (region_county, percentage)%>%
rename (county = "region_county", percent_lowbirthweight = "percentage")
This has 87 rows and 2 columns of data. In which, 2 variables
are:
- county: NY county name
- percent_lowbirthweight: percentage of children being born
being identified as low birth weight (<2,500g) (https://www.health.ny.gov/)
The following dataset is obtained from New York State Department of Health (https://www.health.ny.gov/) that contains 4 different health indicators that will complement with the low birthweight. These 5 will act as our outcomes of interest for our regression models.
health <- health %>%
janitor::clean_names()%>%
select (geographic_area,indicator_title,topic_area,rate_percent,measurement) %>%
filter( str_detect (geographic_area, " County")) %>%
mutate (county = str_replace (geographic_area, " County", "")) %>%
select (county, everything()) %>%
select (-geographic_area) %>%
filter(topic_area == "Cancer Indicators" | topic_area == "Respiratory Disease Indicators" | topic_area == "Cardiovascular Disease Indicators" | topic_area == "Maternal and Infant Health Indicators")
cancer <- health %>%
filter (topic_area == "Cancer Indicators") %>%
filter (indicator_title == "All cancer incidence rate per 100,000") %>%
select (-c(indicator_title, topic_area, measurement)) %>%
rename (cancer_mortality_per_100k = "rate_percent")
resp <- health %>%
filter (topic_area == "Respiratory Disease Indicators") %>%
filter (indicator_title == "Asthma hospitalization rate per 10,000") %>%
select (-c(indicator_title, topic_area, measurement)) %>%
rename (asthma_hosp_rate_per_10k = "rate_percent")
cardio <- health %>%
filter (topic_area == "Cardiovascular Disease Indicators") %>%
filter (indicator_title == "Cardiovascular disease hospitalization rate per 10,000") %>%
select (-c(indicator_title, topic_area, measurement)) %>%
rename (cardio_hosp_rate_per_10k = "rate_percent")
maternal <- health %>%
filter (topic_area == "Maternal and Infant Health Indicators") %>%
filter (indicator_title == "Percentage of premature births with <37 weeks gestation") %>%
select (-c(indicator_title, topic_area, measurement)) %>%
rename (premature_percentage = "rate_percent")
They are:
- cancer_mortality_per_100k: percentage of cancer mortality
per 100 thousands people in each NY county (Cancer Indicator)
- asthma_hosp_rate_per_10k: percentage of asthma
hospitalization per 10 thousands people in each NY county (Respiratory
Disease Indicator)
- cardio_hosp_rate_per_10k“: percentage of
cardiovascular-disease-related hospitalization per 10 thousands people
in each NY county (Cardiovascular Disease Indicator)
- premature_percentage: percentage of children being born
prematurely (<37 gestational weeks) in each NY county
Here we perform inner_join() to create 2 bigger datasets
health_merge and demographic_merge. Then, we
join them with our lowbirthweight & pm2_5
tp make a finalized data frame called merge. And, we will
use this for regression model.
health_merge <- maternal %>%
inner_join(cancer, by = "county") %>%
inner_join(resp, by = "county") %>%
inner_join(cardio, by = "county")
demographic_merge <- age %>%
inner_join(sex, by = "county") %>%
inner_join(income, by = "county") %>%
inner_join(race_merge, by = "county") %>%
inner_join(edu, by = "county") %>%
mutate(county = str_replace (county, "St ", "St. "))
merge <- lowbirthweight %>%
inner_join(pm2_5, by = "county") %>%
inner_join(health_merge, by = "county") %>%
inner_join(demographic_merge, by = "county")
merge <- merge %>%
select (county, annual_pm2.5,everything())%>%
mutate(asthma_hosp_rate_per_10k = as.numeric(asthma_hosp_rate_per_10k))%>%
mutate(cardio_hosp_rate_per_10k = as.numeric(cardio_hosp_rate_per_10k)) %>%
mutate(premature_percentage = as.numeric(premature_percentage))%>%
mutate(cancer_mortality_per_100k = as.numeric(cancer_mortality_per_100k))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `asthma_hosp_rate_per_10k =
## as.numeric(asthma_hosp_rate_per_10k)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `premature_percentage = as.numeric(premature_percentage)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `cancer_mortality_per_100k =
## as.numeric(cancer_mortality_per_100k)`.
## Caused by warning:
## ! NAs introduced by coercion
uscounties <- uscounties %>%
filter (state_id == "NY") %>%
select (county, lat, lng)
map <- merge %>%
inner_join(uscounties, by ="county")
write.csv(map, "NY_map.csv", row.names = FALSE)
merge %>%
plot_ly(
x = ~reorder(county, percent_high_income),
y = ~percent_high_income,
type = "bar",
marker = list(color = "red1")
) %>%
layout(
title = "Percentage of High Income",
xaxis = list(title = "County Name", categoryorder = "total descending"),
yaxis = list(title = "Percentage"),
barmode = "stack"
)
race_plot <- merge %>%
select (county, percent_non_hisp_white, percent_non_hisp_black, percent_hisp_white, percent_hisp_black, percent_other) %>%
pivot_longer(
cols = starts_with("percent_"), names_to = "race", values_to = "percentage")
race_plot%>%
plot_ly(x = ~county, y = ~percentage, type = "bar",color = ~race,colors = "RdYlGn", hoverinfo = "y+name") %>%
layout(barmode = "stack",
title = "Racial Composition in NY county",
xaxis = list(title = "County"),
yaxis = list(title = "Percentage (%)"))
merge %>%
drop_na() %>%
plot_ly(
x = ~county,
y = ~percent_lowbirthweight,
color = ~county,
type = "scatter",
mode = "markers"
) %>%
layout(
title = "Rate of Low Birthweight",
xaxis = list(title = "County", tickangle = 90),
yaxis = list(title = "Percentage of Low Birthweight"),
showlegend = FALSE
)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
merge %>%
drop_na() %>%
plot_ly(
x = ~county,
y = ~premature_percentage,
color = ~county,
type = "scatter",
mode = "markers"
) %>%
layout(
title = "Rate of Premature Birth",
xaxis = list(title = "County", tickangle = 90),
yaxis = list(title = "Rate of Premature Birth"),
showlegend = FALSE
)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
merge %>%
drop_na() %>%
plot_ly(
x = ~county,
y = ~asthma_hosp_rate_per_10k,
color = ~county,
type = "scatter",
mode = "markers"
) %>%
layout(
title = "Rate of Asthma Hospitalization",
xaxis = list(title = "County", tickangle = 90),
yaxis = list(title = "Rate per 10k"),
showlegend = FALSE
)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
merge %>%
drop_na() %>%
plot_ly(
x = ~county,
y = ~cancer_mortality_per_100k,
color = ~county,
type = "scatter",
mode = "markers"
) %>%
layout(
title = "Rate of Cancer Mortality",
xaxis = list(title = "County", tickangle = 90),
yaxis = list(title = "Rate per 100k"),
showlegend = FALSE
)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
merge %>%
drop_na() %>%
plot_ly(
x = ~county,
y = ~cardio_hosp_rate_per_10k,
color = ~county,
type = "scatter",
mode = "markers"
) %>%
layout(
title = "Rate of Cardiovascular Disease Hospitalization",
xaxis = list(title = "County", tickangle = 90),
yaxis = list(title = "Rate per 10k"),
showlegend = FALSE
)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
lm_pm2.5_birthweight <- lm(percent_lowbirthweight~annual_pm2.5 , data=merge)
summary(lm_pm2.5_birthweight)
##
## Call:
## lm(formula = percent_lowbirthweight ~ annual_pm2.5, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7519 -0.5694 0.1806 0.9024 2.4051
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0310 1.2493 3.227 0.00203 **
## annual_pm2.5 0.4535 0.1766 2.567 0.01276 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.454 on 60 degrees of freedom
## Multiple R-squared: 0.09898, Adjusted R-squared: 0.08396
## F-statistic: 6.591 on 1 and 60 DF, p-value: 0.01276
lm_pm2.5_birthweight_adjusted <-lm(percent_lowbirthweight ~ annual_pm2.5 + median_age + percent_high_income + percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white + percent_hisp_black + percentage_high_education + percent_male, data=merge)
summary(lm_pm2.5_birthweight_adjusted)
##
## Call:
## lm(formula = percent_lowbirthweight ~ annual_pm2.5 + median_age +
## percent_high_income + percent_non_hisp_white + percent_non_hisp_black +
## percent_hisp_white + percent_hisp_black + percentage_high_education +
## percent_male, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9906 -0.5299 0.2656 0.7311 2.0472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.26648 10.33015 0.510 0.612
## annual_pm2.5 0.26854 0.22070 1.217 0.229
## median_age -0.10337 0.05587 -1.850 0.070 .
## percent_high_income -3.98469 4.15411 -0.959 0.342
## percent_non_hisp_white 3.04094 4.86581 0.625 0.535
## percent_non_hisp_black 12.77635 9.34871 1.367 0.178
## percent_hisp_white 17.09556 17.91459 0.954 0.344
## percent_hisp_black -15.49031 43.02226 -0.360 0.720
## percentage_high_education 0.11608 3.51768 0.033 0.974
## percent_male 5.00262 15.93902 0.314 0.755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.362 on 52 degrees of freedom
## Multiple R-squared: 0.3148, Adjusted R-squared: 0.1962
## F-statistic: 2.654 on 9 and 52 DF, p-value: 0.01303
step(lm_pm2.5_birthweight_adjusted, direction = 'both')
## Start: AIC=47.43
## percent_lowbirthweight ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black + percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percentage_high_education 1 0.0020 96.507 45.434
## - percent_male 1 0.1828 96.688 45.550
## - percent_hisp_black 1 0.2406 96.746 45.587
## - percent_non_hisp_white 1 0.7249 97.230 45.897
## - percent_hisp_white 1 1.6901 98.195 46.509
## - percent_high_income 1 1.7076 98.213 46.520
## - annual_pm2.5 1 2.7476 99.253 47.173
## <none> 96.505 47.433
## - percent_non_hisp_black 1 3.4662 99.972 47.621
## - median_age 1 6.3530 102.858 49.386
##
## Step: AIC=45.43
## percent_lowbirthweight ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black + percent_male
##
## Df Sum of Sq RSS AIC
## - percent_male 1 0.1910 96.698 43.557
## - percent_hisp_black 1 0.2405 96.748 43.588
## - percent_non_hisp_white 1 0.7548 97.262 43.917
## - percent_hisp_white 1 1.9357 98.443 44.665
## - annual_pm2.5 1 2.7457 99.253 45.173
## <none> 96.507 45.434
## - percent_high_income 1 3.3754 99.883 45.566
## - percent_non_hisp_black 1 3.5113 100.019 45.650
## + percentage_high_education 1 0.0020 96.505 47.433
## - median_age 1 7.4111 103.918 48.021
##
## Step: AIC=43.56
## percent_lowbirthweight ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black
##
## Df Sum of Sq RSS AIC
## - percent_hisp_black 1 0.3373 97.036 41.773
## - percent_non_hisp_white 1 0.7998 97.498 42.067
## - percent_hisp_white 1 2.1084 98.807 42.894
## - annual_pm2.5 1 2.7154 99.414 43.274
## <none> 96.698 43.557
## - percent_non_hisp_black 1 3.4767 100.175 43.747
## - percent_high_income 1 3.9879 100.686 44.062
## + percent_male 1 0.1910 96.507 45.434
## + percentage_high_education 1 0.0102 96.688 45.550
## - median_age 1 7.2653 103.964 46.048
##
## Step: AIC=41.77
## percent_lowbirthweight ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white
##
## Df Sum of Sq RSS AIC
## - percent_non_hisp_white 1 1.2535 98.289 40.568
## - percent_hisp_white 1 1.9333 98.969 40.996
## - annual_pm2.5 1 2.7548 99.791 41.508
## <none> 97.036 41.773
## - percent_non_hisp_black 1 3.3156 100.351 41.856
## - percent_high_income 1 3.7372 100.773 42.116
## + percent_hisp_black 1 0.3373 96.698 43.557
## + percent_male 1 0.2879 96.748 43.588
## + percentage_high_education 1 0.0191 97.017 43.760
## - median_age 1 7.4932 104.529 44.384
##
## Step: AIC=40.57
## percent_lowbirthweight ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_black + percent_hisp_white
##
## Df Sum of Sq RSS AIC
## - percent_hisp_white 1 0.6890 98.978 39.001
## - annual_pm2.5 1 1.9873 100.277 39.809
## - percent_non_hisp_black 1 2.9014 101.191 40.372
## - percent_high_income 1 3.1909 101.480 40.549
## <none> 98.289 40.568
## + percent_non_hisp_white 1 1.2535 97.036 41.773
## + percent_hisp_black 1 0.7910 97.498 42.067
## + percent_male 1 0.4365 97.853 42.292
## + percentage_high_education 1 0.2070 98.082 42.438
## - median_age 1 7.2274 105.517 42.968
##
## Step: AIC=39
## percent_lowbirthweight ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_black
##
## Df Sum of Sq RSS AIC
## - annual_pm2.5 1 2.1570 101.135 38.338
## - percent_high_income 1 2.5040 101.482 38.550
## <none> 98.978 39.001
## + percent_hisp_white 1 0.6890 98.289 40.568
## + percentage_high_education 1 0.4551 98.523 40.716
## + percent_male 1 0.4461 98.532 40.721
## + percent_hisp_black 1 0.1537 98.824 40.905
## - percent_non_hisp_black 1 6.5238 105.502 40.959
## + percent_non_hisp_white 1 0.0091 98.969 40.996
## - median_age 1 7.6698 106.648 41.629
##
## Step: AIC=38.34
## percent_lowbirthweight ~ median_age + percent_high_income + percent_non_hisp_black
##
## Df Sum of Sq RSS AIC
## - percent_high_income 1 1.1669 102.302 37.049
## <none> 101.135 38.338
## + annual_pm2.5 1 2.1570 98.978 39.001
## + percent_hisp_white 1 0.8587 100.277 39.809
## + percentage_high_education 1 0.3690 100.766 40.111
## + percent_male 1 0.3526 100.783 40.122
## + percent_hisp_black 1 0.0682 101.067 40.296
## + percent_non_hisp_white 1 0.0581 101.077 40.302
## - median_age 1 8.6493 109.785 41.426
## - percent_non_hisp_black 1 11.1390 112.274 42.816
##
## Step: AIC=37.05
## percent_lowbirthweight ~ median_age + percent_non_hisp_black
##
## Df Sum of Sq RSS AIC
## <none> 102.30 37.049
## + percentage_high_education 1 1.4733 100.83 38.150
## + percent_high_income 1 1.1669 101.14 38.338
## + annual_pm2.5 1 0.8200 101.48 38.550
## + percent_male 1 0.6572 101.64 38.650
## + percent_non_hisp_white 1 0.0433 102.26 39.023
## + percent_hisp_white 1 0.0400 102.26 39.025
## + percent_hisp_black 1 0.0005 102.30 39.049
## - median_age 1 9.2828 111.58 40.434
## - percent_non_hisp_black 1 10.0049 112.31 40.834
##
## Call:
## lm(formula = percent_lowbirthweight ~ median_age + percent_non_hisp_black,
## data = merge)
##
## Coefficients:
## (Intercept) median_age percent_non_hisp_black
## 11.5269 -0.1142 8.0498
lm_pm2.5_birthweight_adjusted_best <- lm(percent_lowbirthweight~median_age + percent_non_hisp_black, data=merge)
summary (lm_pm2.5_birthweight_adjusted_best) %>%
tab_model()
| percent lowbirthweight | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 11.53 | 7.18 – 15.88 | <0.001 |
| median age | -0.11 | -0.21 – -0.02 | 0.024 |
| percent non hisp black | 8.05 | 1.34 – 14.76 | 0.019 |
| Observations | 62 | ||
| R2 / R2 adjusted | 0.274 / 0.249 | ||
lm_pm2.5_premature <- lm(premature_percentage ~ annual_pm2.5 , data=merge)
summary(lm_pm2.5_premature)
##
## Call:
## lm(formula = premature_percentage ~ annual_pm2.5, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3611 -0.3666 0.0279 0.6960 2.1960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.9789 0.9329 7.481 4.15e-10 ***
## annual_pm2.5 0.2637 0.1316 2.004 0.0497 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.075 on 59 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.06372, Adjusted R-squared: 0.04785
## F-statistic: 4.015 on 1 and 59 DF, p-value: 0.04969
lm_pm2.5_premature_adjusted <-lm(premature_percentage ~ annual_pm2.5 + median_age + percent_high_income + percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white + percent_hisp_black + percentage_high_education + percent_male, data=merge)
summary(lm_pm2.5_premature_adjusted)
##
## Call:
## lm(formula = premature_percentage ~ annual_pm2.5 + median_age +
## percent_high_income + percent_non_hisp_white + percent_non_hisp_black +
## percent_hisp_white + percent_hisp_black + percentage_high_education +
## percent_male, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4709 -0.3881 0.0150 0.5947 1.9423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.35484 8.04591 -0.541 0.5907
## annual_pm2.5 0.28875 0.17252 1.674 0.1003
## median_age 0.07759 0.04906 1.581 0.1200
## percent_high_income -1.67757 3.23574 -0.518 0.6064
## percent_non_hisp_white 4.08346 3.82446 1.068 0.2907
## percent_non_hisp_black 15.18062 7.28782 2.083 0.0423 *
## percent_hisp_white 13.74174 14.02625 0.980 0.3319
## percent_hisp_black -8.02314 33.51172 -0.239 0.8117
## percentage_high_education -0.72087 2.74036 -0.263 0.7936
## percent_male 8.73135 12.42414 0.703 0.4854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.061 on 51 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2123, Adjusted R-squared: 0.07328
## F-statistic: 1.527 on 9 and 51 DF, p-value: 0.1638
step(lm_pm2.5_premature_adjusted, direction = 'both')
## Start: AIC=16.3
## premature_percentage ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black + percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percent_hisp_black 1 0.0645 57.475 14.369
## - percentage_high_education 1 0.0779 57.488 14.383
## - percent_high_income 1 0.3026 57.713 14.621
## - percent_male 1 0.5560 57.966 14.888
## - percent_hisp_white 1 1.0805 58.491 15.438
## - percent_non_hisp_white 1 1.2833 58.694 15.649
## <none> 57.410 16.300
## - median_age 1 2.8154 60.226 17.221
## - annual_pm2.5 1 3.1536 60.564 17.562
## - percent_non_hisp_black 1 4.8843 62.295 19.281
##
## Step: AIC=14.37
## premature_percentage ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percentage_high_education 1 0.0782 57.553 12.452
## - percent_high_income 1 0.2455 57.720 12.629
## - percent_male 1 0.6430 58.118 13.048
## - percent_hisp_white 1 1.0302 58.505 13.453
## - percent_non_hisp_white 1 1.5623 59.037 14.005
## <none> 57.475 14.369
## - median_age 1 2.7685 60.243 15.239
## - annual_pm2.5 1 3.1781 60.653 15.652
## + percent_hisp_black 1 0.0645 57.410 16.300
## - percent_non_hisp_black 1 4.8257 62.301 17.287
##
## Step: AIC=12.45
## premature_percentage ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_male
##
## Df Sum of Sq RSS AIC
## - percent_male 1 0.9031 58.456 11.402
## - percent_high_income 1 1.1751 58.728 11.685
## - percent_hisp_white 1 1.4917 59.045 12.013
## - percent_non_hisp_white 1 1.8899 59.443 12.423
## <none> 57.553 12.452
## - annual_pm2.5 1 3.2023 60.755 13.755
## - median_age 1 3.3963 60.949 13.950
## + percentage_high_education 1 0.0782 57.475 14.369
## + percent_hisp_black 1 0.0649 57.488 14.383
## - percent_non_hisp_black 1 5.1011 62.654 15.632
##
## Step: AIC=11.4
## premature_percentage ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white
##
## Df Sum of Sq RSS AIC
## - percent_high_income 1 1.6111 60.067 11.060
## - percent_hisp_white 1 1.7178 60.174 11.168
## <none> 58.456 11.402
## - percent_non_hisp_white 1 2.2100 60.666 11.665
## + percent_male 1 0.9031 57.553 12.452
## - annual_pm2.5 1 3.1340 61.590 12.588
## + percentage_high_education 1 0.3384 58.118 13.048
## - median_age 1 3.6934 62.150 13.139
## + percent_hisp_black 1 0.1812 58.275 13.212
## - percent_non_hisp_black 1 4.9412 63.398 14.352
##
## Step: AIC=11.06
## premature_percentage ~ annual_pm2.5 + median_age + percent_non_hisp_white +
## percent_non_hisp_black + percent_hisp_white
##
## Df Sum of Sq RSS AIC
## - percent_hisp_white 1 0.7258 60.793 9.7929
## - percent_non_hisp_white 1 1.7200 61.787 10.7824
## <none> 60.067 11.0603
## - annual_pm2.5 1 2.0150 62.083 11.0730
## + percentage_high_education 1 1.8034 58.264 11.2008
## + percent_high_income 1 1.6111 58.456 11.4018
## + percent_male 1 1.3392 58.728 11.6850
## - median_age 1 3.2009 63.268 12.2272
## + percent_hisp_black 1 0.0234 60.044 13.0365
## - percent_non_hisp_black 1 4.7825 64.850 13.7334
##
## Step: AIC=9.79
## premature_percentage ~ annual_pm2.5 + median_age + percent_non_hisp_white +
## percent_non_hisp_black
##
## Df Sum of Sq RSS AIC
## - percent_non_hisp_white 1 1.0724 61.866 8.8596
## - annual_pm2.5 1 1.9476 62.741 9.7165
## <none> 60.793 9.7929
## + percentage_high_education 1 1.6925 59.101 10.0706
## + percent_male 1 1.3682 59.425 10.4044
## + percent_hisp_white 1 0.7258 60.067 11.0603
## + percent_high_income 1 0.6191 60.174 11.1685
## - median_age 1 3.5247 64.318 11.2309
## + percent_hisp_black 1 0.0078 60.785 11.7851
## - percent_non_hisp_black 1 4.1729 64.966 11.8426
##
## Step: AIC=8.86
## premature_percentage ~ annual_pm2.5 + median_age + percent_non_hisp_black
##
## Df Sum of Sq RSS AIC
## - annual_pm2.5 1 1.2076 63.073 8.0388
## + percentage_high_education 1 2.1665 59.699 8.6851
## <none> 61.866 8.8596
## + percent_male 1 1.6360 60.230 9.2248
## + percent_high_income 1 1.1257 60.740 9.7395
## + percent_non_hisp_white 1 1.0724 60.793 9.7929
## - median_age 1 3.9005 65.766 10.5891
## + percent_hisp_black 1 0.1279 61.738 10.7333
## + percent_hisp_white 1 0.0782 61.787 10.7824
## - percent_non_hisp_black 1 5.2522 67.118 11.8302
##
## Step: AIC=8.04
## premature_percentage ~ median_age + percent_non_hisp_black
##
## Df Sum of Sq RSS AIC
## <none> 63.073 8.0388
## + percent_male 1 1.2567 61.817 8.8111
## + annual_pm2.5 1 1.2076 61.866 8.8596
## + percentage_high_education 1 1.0446 62.029 9.0201
## - median_age 1 3.7908 66.864 9.5990
## + percent_non_hisp_white 1 0.3323 62.741 9.7165
## + percent_high_income 1 0.2774 62.796 9.7699
## + percent_hisp_black 1 0.1335 62.940 9.9095
## + percent_hisp_white 1 0.0007 63.073 10.0381
## - percent_non_hisp_black 1 9.6435 72.717 14.7176
##
## Call:
## lm(formula = premature_percentage ~ median_age + percent_non_hisp_black,
## data = merge)
##
## Coefficients:
## (Intercept) median_age percent_non_hisp_black
## 4.8783 0.0838 8.0274
lm_pm2.5_premature_adjusted_best <- lm(premature_percentage ~ median_age + percent_non_hisp_black, data = merge)
summary(lm_pm2.5_premature_adjusted_best)%>%
tab_model()
| premature percentage | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.88 | 0.96 – 8.80 | 0.016 |
| median age | 0.08 | -0.01 – 0.17 | 0.067 |
| percent non hisp black | 8.03 | 2.63 – 13.42 | 0.004 |
| Observations | 61 | ||
| R2 / R2 adjusted | 0.135 / 0.105 | ||
lm_pm2.5_cancer <- lm(cancer_mortality_per_100k ~ annual_pm2.5 , data=merge)
summary(lm_pm2.5_cancer)
##
## Call:
## lm(formula = cancer_mortality_per_100k ~ annual_pm2.5, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -206.55 -34.46 1.63 45.56 154.59
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 871.126 65.539 13.292 < 2e-16 ***
## annual_pm2.5 -27.255 9.245 -2.948 0.00458 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 75.55 on 59 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1284, Adjusted R-squared: 0.1136
## F-statistic: 8.691 on 1 and 59 DF, p-value: 0.004576
lm_pm2.5_cancer_adjusted <-lm(cancer_mortality_per_100k ~annual_pm2.5 + median_age + percent_high_income + percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white + percent_hisp_black + percentage_high_education + percent_male, data=merge)
summary(lm_pm2.5_cancer_adjusted)
##
## Call:
## lm(formula = cancer_mortality_per_100k ~ annual_pm2.5 + median_age +
## percent_high_income + percent_non_hisp_white + percent_non_hisp_black +
## percent_hisp_white + percent_hisp_black + percentage_high_education +
## percent_male, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -82.144 -23.468 3.255 24.875 71.915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.336 287.601 0.192 0.84819
## annual_pm2.5 13.203 6.167 2.141 0.03707 *
## median_age 12.889 1.754 7.349 1.53e-09 ***
## percent_high_income -175.488 115.661 -1.517 0.13538
## percent_non_hisp_white 448.039 136.705 3.277 0.00189 **
## percent_non_hisp_black 519.597 260.503 1.995 0.05145 .
## percent_hisp_white 935.183 501.368 1.865 0.06790 .
## percent_hisp_black -1786.174 1197.876 -1.491 0.14209
## percentage_high_education -44.671 97.954 -0.456 0.65029
## percent_male -640.838 444.101 -1.443 0.15513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37.92 on 51 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.8101, Adjusted R-squared: 0.7766
## F-statistic: 24.18 on 9 and 51 DF, p-value: 1.897e-15
step(lm_pm2.5_cancer_adjusted, direction = 'both')
## Start: AIC=452.62
## cancer_mortality_per_100k ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black + percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percentage_high_education 1 299 73653 450.87
## <none> 73354 452.62
## - percent_male 1 2995 76348 453.06
## - percent_hisp_black 1 3198 76552 453.23
## - percent_high_income 1 3311 76665 453.32
## - percent_hisp_white 1 5004 78358 454.65
## - percent_non_hisp_black 1 5722 79076 455.20
## - annual_pm2.5 1 6593 79947 455.87
## - percent_non_hisp_white 1 15449 88803 462.28
## - median_age 1 77687 151040 494.68
##
## Step: AIC=450.87
## cancer_mortality_per_100k ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black + percent_male
##
## Df Sum of Sq RSS AIC
## <none> 73653 450.87
## - percent_male 1 2696 76349 451.06
## - percent_hisp_black 1 3203 76855 451.47
## + percentage_high_education 1 299 73354 452.62
## - percent_non_hisp_black 1 6218 79871 453.81
## - annual_pm2.5 1 6660 80313 454.15
## - percent_hisp_white 1 7058 80711 454.45
## - percent_high_income 1 10188 83840 456.77
## - percent_non_hisp_white 1 17739 91392 462.03
## - median_age 1 89363 163016 497.33
##
## Call:
## lm(formula = cancer_mortality_per_100k ~ annual_pm2.5 + median_age +
## percent_high_income + percent_non_hisp_white + percent_non_hisp_black +
## percent_hisp_white + percent_hisp_black + percent_male, data = merge)
##
## Coefficients:
## (Intercept) annual_pm2.5 median_age
## -1.845 13.266 13.138
## percent_high_income percent_non_hisp_white percent_non_hisp_black
## -213.487 464.030 536.278
## percent_hisp_white percent_hisp_black percent_male
## 1023.820 -1787.433 -574.147
lm_pm2.5_cancer_adjusted_best <- lm(cancer_mortality_per_100k ~ annual_pm2.5 + median_age + percent_high_income + percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white + percent_hisp_black + percent_male, data =merge)
summary (lm_pm2.5_cancer_adjusted_best)%>%
tab_model()
|
cancer mortality per 100 k |
|||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -1.85 | -517.25 – 513.56 | 0.994 |
| annual pm2 5 | 13.27 | 0.99 – 25.54 | 0.035 |
| median age | 13.14 | 9.82 – 16.46 | <0.001 |
| percent high income | -213.49 | -373.22 – -53.75 | 0.010 |
| percent non hisp white | 464.03 | 200.92 – 727.14 | 0.001 |
| percent non hisp black | 536.28 | 22.68 – 1049.88 | 0.041 |
| percent hisp white | 1023.82 | 103.51 – 1944.13 | 0.030 |
| percent hisp black | -1787.43 | -4172.76 – 597.90 | 0.139 |
| percent male | -574.15 | -1409.17 – 260.87 | 0.174 |
| Observations | 61 | ||
| R2 / R2 adjusted | 0.809 / 0.780 | ||
lm_pm2.5_asthma <- lm(asthma_hosp_rate_per_10k ~ annual_pm2.5 , data=merge)
summary(lm_pm2.5_asthma)
##
## Call:
## lm(formula = asthma_hosp_rate_per_10k ~ annual_pm2.5, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3861 -1.4748 -0.7693 0.6846 21.8951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.630 3.376 -2.260 0.027646 *
## annual_pm2.5 1.703 0.472 3.608 0.000651 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.415 on 57 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1859, Adjusted R-squared: 0.1717
## F-statistic: 13.02 on 1 and 57 DF, p-value: 0.0006507
lm_pm2.5_asthma_adjusted <-lm(asthma_hosp_rate_per_10k ~ annual_pm2.5 + median_age + percent_high_income + percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white + percent_hisp_black + percentage_high_education + percent_male, data=merge)
summary(lm_pm2.5_asthma_adjusted)
##
## Call:
## lm(formula = asthma_hosp_rate_per_10k ~ annual_pm2.5 + median_age +
## percent_high_income + percent_non_hisp_white + percent_non_hisp_black +
## percent_hisp_white + percent_hisp_black + percentage_high_education +
## percent_male, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7456 -0.6676 -0.1249 0.7388 2.7314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.13088 7.84811 0.526 0.6010
## annual_pm2.5 -0.08611 0.20011 -0.430 0.6689
## median_age 0.06249 0.05082 1.230 0.2247
## percent_high_income 3.53198 3.08690 1.144 0.2581
## percent_non_hisp_white 1.08237 3.67392 0.295 0.7695
## percent_non_hisp_black 16.53403 6.95374 2.378 0.0214 *
## percent_hisp_white 5.65133 13.45394 0.420 0.6763
## percent_hisp_black 362.48923 32.21071 11.254 3.46e-15 ***
## percentage_high_education -4.60085 2.73077 -1.685 0.0984 .
## percent_male -11.30604 12.41573 -0.911 0.3670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 49 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.9386, Adjusted R-squared: 0.9273
## F-statistic: 83.25 on 9 and 49 DF, p-value: < 2.2e-16
step(lm_pm2.5_asthma_adjusted, direction = 'both')
## Start: AIC=10.38
## asthma_hosp_rate_per_10k ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black + percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percent_non_hisp_white 1 0.089 50.215 8.488
## - percent_hisp_white 1 0.180 50.307 8.596
## - annual_pm2.5 1 0.189 50.316 8.606
## - percent_male 1 0.848 50.975 9.374
## - percent_high_income 1 1.339 51.466 9.939
## - median_age 1 1.547 51.673 10.176
## <none> 50.126 10.383
## - percentage_high_education 1 2.904 53.030 11.706
## - percent_non_hisp_black 1 5.783 55.910 14.826
## - percent_hisp_black 1 129.556 179.683 83.706
##
## Step: AIC=8.49
## asthma_hosp_rate_per_10k ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_black + percent_hisp_white + percent_hisp_black +
## percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percent_hisp_white 1 0.092 50.307 6.596
## - annual_pm2.5 1 0.295 50.510 6.833
## - percent_male 1 0.850 51.065 7.479
## - percent_high_income 1 1.531 51.746 8.260
## - median_age 1 1.609 51.824 8.349
## <none> 50.215 8.488
## - percentage_high_education 1 3.267 53.482 10.206
## + percent_non_hisp_white 1 0.089 50.126 10.383
## - percent_non_hisp_black 1 12.432 62.647 19.539
## - percent_hisp_black 1 136.612 186.827 84.006
##
## Step: AIC=6.6
## asthma_hosp_rate_per_10k ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_black + percent_hisp_black + percentage_high_education +
## percent_male
##
## Df Sum of Sq RSS AIC
## - annual_pm2.5 1 0.285 50.592 4.929
## - percent_male 1 0.851 51.158 5.586
## - median_age 1 1.530 51.837 6.363
## <none> 50.307 6.596
## - percent_high_income 1 3.275 53.582 8.317
## + percent_hisp_white 1 0.092 50.215 8.488
## + percent_non_hisp_white 1 0.000 50.307 8.596
## - percentage_high_education 1 3.894 54.201 8.994
## - percent_non_hisp_black 1 13.111 63.418 18.260
## - percent_hisp_black 1 181.546 231.853 94.745
##
## Step: AIC=4.93
## asthma_hosp_rate_per_10k ~ median_age + percent_high_income +
## percent_non_hisp_black + percent_hisp_black + percentage_high_education +
## percent_male
##
## Df Sum of Sq RSS AIC
## - percent_male 1 1.030 51.622 4.118
## - median_age 1 1.349 51.941 4.482
## <none> 50.592 4.929
## - percent_high_income 1 3.039 53.631 6.371
## + annual_pm2.5 1 0.285 50.307 6.596
## + percent_hisp_white 1 0.082 50.510 6.833
## + percent_non_hisp_white 1 0.012 50.580 6.915
## - percentage_high_education 1 4.599 55.191 8.063
## - percent_non_hisp_black 1 12.967 63.559 16.392
## - percent_hisp_black 1 184.303 234.895 93.515
##
## Step: AIC=4.12
## asthma_hosp_rate_per_10k ~ median_age + percent_high_income +
## percent_non_hisp_black + percent_hisp_black + percentage_high_education
##
## Df Sum of Sq RSS AIC
## - median_age 1 1.616 53.238 3.936
## <none> 51.622 4.118
## + percent_male 1 1.030 50.592 4.929
## - percent_high_income 1 2.616 54.238 5.035
## + annual_pm2.5 1 0.463 51.158 5.586
## + percent_hisp_white 1 0.081 51.541 6.025
## + percent_non_hisp_white 1 0.022 51.600 6.093
## - percentage_high_education 1 3.605 55.226 6.100
## - percent_non_hisp_black 1 13.597 65.219 15.913
## - percent_hisp_black 1 199.425 251.047 95.438
##
## Step: AIC=3.94
## asthma_hosp_rate_per_10k ~ percent_high_income + percent_non_hisp_black +
## percent_hisp_black + percentage_high_education
##
## Df Sum of Sq RSS AIC
## <none> 53.238 3.936
## + median_age 1 1.616 51.622 4.118
## + percent_male 1 1.296 51.941 4.482
## + annual_pm2.5 1 0.221 53.016 5.691
## + percent_non_hisp_white 1 0.092 53.146 5.835
## + percent_hisp_white 1 0.008 53.229 5.927
## - percent_high_income 1 4.568 57.805 6.793
## - percentage_high_education 1 6.197 59.434 8.433
## - percent_non_hisp_black 1 12.074 65.311 13.996
## - percent_hisp_black 1 198.347 251.584 93.564
##
## Call:
## lm(formula = asthma_hosp_rate_per_10k ~ percent_high_income +
## percent_non_hisp_black + percent_hisp_black + percentage_high_education,
## data = merge)
##
## Coefficients:
## (Intercept) percent_high_income
## 1.362 4.653
## percent_non_hisp_black percent_hisp_black
## 13.518 366.672
## percentage_high_education
## -5.306
lm_pm2.5_asthma_adjusted_best <- lm(asthma_hosp_rate_per_10k~ percent_high_income + percent_non_hisp_black + percent_hisp_black + percentage_high_education, data=merge)
summary(lm_pm2.5_asthma_adjusted_best)%>%
tab_model()
| asthma hosp rate per 10 k | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.36 | 0.06 – 2.67 | 0.041 |
| percent high income | 4.65 | 0.32 – 8.99 | 0.036 |
| percent non hisp black | 13.52 | 5.77 – 21.26 | 0.001 |
| percent hisp black | 366.67 | 314.84 – 418.50 | <0.001 |
| percentage high education | -5.31 | -9.55 – -1.06 | 0.015 |
| Observations | 59 | ||
| R2 / R2 adjusted | 0.935 / 0.930 | ||
lm_pm2.5_cardio <- lm(cardio_hosp_rate_per_10k ~ annual_pm2.5 , data=merge)
summary(lm_pm2.5_cardio)
##
## Call:
## lm(formula = cardio_hosp_rate_per_10k ~ annual_pm2.5, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -69.983 -11.516 4.183 18.558 49.049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 144.4652 22.3939 6.451 2.14e-08 ***
## annual_pm2.5 0.9545 3.1662 0.301 0.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.07 on 60 degrees of freedom
## Multiple R-squared: 0.001512, Adjusted R-squared: -0.01513
## F-statistic: 0.09088 on 1 and 60 DF, p-value: 0.7641
lm_pm2.5_cardio_adjusted <-lm(cardio_hosp_rate_per_10k ~annual_pm2.5 + median_age + percent_high_income + percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white + percent_hisp_black + percentage_high_education + percent_male, data=merge)
summary(lm_pm2.5_cardio_adjusted)
##
## Call:
## lm(formula = cardio_hosp_rate_per_10k ~ annual_pm2.5 + median_age +
## percent_high_income + percent_non_hisp_white + percent_non_hisp_black +
## percent_hisp_white + percent_hisp_black + percentage_high_education +
## percent_male, data = merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.098 -8.011 3.779 12.581 33.035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 183.1675 172.5353 1.062 0.2933
## annual_pm2.5 7.2209 3.6862 1.959 0.0555 .
## median_age 1.9151 0.9331 2.052 0.0452 *
## percent_high_income -44.4537 69.3825 -0.641 0.5245
## percent_non_hisp_white 106.5411 81.2693 1.311 0.1956
## percent_non_hisp_black 241.3852 156.1433 1.546 0.1282
## percent_hisp_white 537.8787 299.2115 1.798 0.0780 .
## percent_hisp_black -640.7688 718.5628 -0.892 0.3766
## percentage_high_education -131.5332 58.7527 -2.239 0.0295 *
## percent_male -432.0543 266.2154 -1.623 0.1106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.75 on 52 degrees of freedom
## Multiple R-squared: 0.3407, Adjusted R-squared: 0.2266
## F-statistic: 2.986 on 9 and 52 DF, p-value: 0.00609
step(lm_pm2.5_cardio_adjusted, direction = 'both')
## Start: AIC=396.56
## cardio_hosp_rate_per_10k ~ annual_pm2.5 + median_age + percent_high_income +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percent_hisp_black + percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percent_high_income 1 212.52 27134 395.05
## - percent_hisp_black 1 411.68 27333 395.50
## <none> 26921 396.56
## - percent_non_hisp_white 1 889.76 27811 396.58
## - percent_non_hisp_black 1 1237.27 28159 397.35
## - percent_male 1 1363.65 28285 397.62
## - percent_hisp_white 1 1673.03 28594 398.30
## - annual_pm2.5 1 1986.63 28908 398.97
## - median_age 1 2180.60 29102 399.39
## - percentage_high_education 1 2594.81 29516 400.26
##
## Step: AIC=395.05
## cardio_hosp_rate_per_10k ~ annual_pm2.5 + median_age + percent_non_hisp_white +
## percent_non_hisp_black + percent_hisp_white + percent_hisp_black +
## percentage_high_education + percent_male
##
## Df Sum of Sq RSS AIC
## - percent_hisp_black 1 274.1 27408 393.67
## - percent_non_hisp_white 1 757.5 27891 394.75
## <none> 27134 395.05
## - percent_non_hisp_black 1 1120.6 28254 395.56
## - percent_male 1 1463.3 28597 396.30
## - percent_hisp_white 1 1568.1 28702 396.53
## + percent_high_income 1 212.5 26921 396.56
## - annual_pm2.5 1 1799.8 28934 397.03
## - median_age 1 1976.3 29110 397.41
## - percentage_high_education 1 7845.9 34980 408.79
##
## Step: AIC=393.67
## cardio_hosp_rate_per_10k ~ annual_pm2.5 + median_age + percent_non_hisp_white +
## percent_non_hisp_black + percent_hisp_white + percentage_high_education +
## percent_male
##
## Df Sum of Sq RSS AIC
## <none> 27408 393.67
## - percent_non_hisp_black 1 1078.3 28486 394.06
## - percent_male 1 1265.2 28673 394.47
## - percent_non_hisp_white 1 1285.6 28693 394.51
## + percent_hisp_black 1 274.1 27134 395.05
## - percent_hisp_white 1 1647.9 29056 395.29
## + percent_high_income 1 75.0 27333 395.50
## - annual_pm2.5 1 1959.6 29367 395.95
## - median_age 1 2053.8 29462 396.15
## - percentage_high_education 1 7775.0 35183 407.15
##
## Call:
## lm(formula = cardio_hosp_rate_per_10k ~ annual_pm2.5 + median_age +
## percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white +
## percentage_high_education + percent_male, data = merge)
##
## Coefficients:
## (Intercept) annual_pm2.5
## 157.519 6.968
## median_age percent_non_hisp_white
## 1.725 117.257
## percent_non_hisp_black percent_hisp_white
## 222.986 439.557
## percentage_high_education percent_male
## -148.614 -405.527
lm_pm2.5_cardio_adjusted_best <-lm(cardio_hosp_rate_per_10k ~annual_pm2.5 + median_age +percent_non_hisp_white + percent_non_hisp_black + percent_hisp_white + percentage_high_education + percent_male, data=merge)
summary (lm_pm2.5_cardio_adjusted_best)%>%
tab_model()
| cardio hosp rate per 10 k | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 157.52 | -155.87 – 470.91 | 0.318 |
| annual pm2 5 | 6.97 | -0.14 – 14.08 | 0.055 |
| median age | 1.73 | 0.01 – 3.44 | 0.049 |
| percent non hisp white | 117.26 | -30.46 – 264.97 | 0.117 |
| percent non hisp black | 222.99 | -83.73 – 529.70 | 0.151 |
| percent hisp white | 439.56 | -49.52 – 928.63 | 0.077 |
| percentage high education | -148.61 | -224.74 – -72.49 | <0.001 |
| percent male | -405.53 | -920.48 – 109.43 | 0.120 |
| Observations | 62 | ||
| R2 / R2 adjusted | 0.329 / 0.242 | ||
tab_model(lm_pm2.5_birthweight_adjusted_best,lm_pm2.5_premature_adjusted_best,lm_pm2.5_cancer_adjusted_best,lm_pm2.5_asthma_adjusted_best,lm_pm2.5_cardio_adjusted )
| percent lowbirthweight | premature percentage |
cancer mortality per 100 k |
asthma hosp rate per 10 k | cardio hosp rate per 10 k | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 11.53 | 7.18 – 15.88 | <0.001 | 4.88 | 0.96 – 8.80 | 0.016 | -1.85 | -517.25 – 513.56 | 0.994 | 1.36 | 0.06 – 2.67 | 0.041 | 183.17 | -163.05 – 529.38 | 0.293 |
| median age | -0.11 | -0.21 – -0.02 | 0.024 | 0.08 | -0.01 – 0.17 | 0.067 | 13.14 | 9.82 – 16.46 | <0.001 | 1.92 | 0.04 – 3.79 | 0.045 | |||
| percent non hisp black | 8.05 | 1.34 – 14.76 | 0.019 | 8.03 | 2.63 – 13.42 | 0.004 | 536.28 | 22.68 – 1049.88 | 0.041 | 13.52 | 5.77 – 21.26 | 0.001 | 241.39 | -71.94 – 554.71 | 0.128 |
| annual pm2 5 | 13.27 | 0.99 – 25.54 | 0.035 | 7.22 | -0.18 – 14.62 | 0.055 | |||||||||
| percent high income | -213.49 | -373.22 – -53.75 | 0.010 | 4.65 | 0.32 – 8.99 | 0.036 | -44.45 | -183.68 – 94.77 | 0.525 | ||||||
| percent non hisp white | 464.03 | 200.92 – 727.14 | 0.001 | 106.54 | -56.54 – 269.62 | 0.196 | |||||||||
| percent hisp white | 1023.82 | 103.51 – 1944.13 | 0.030 | 537.88 | -62.53 – 1138.29 | 0.078 | |||||||||
| percent hisp black | -1787.43 | -4172.76 – 597.90 | 0.139 | 366.67 | 314.84 – 418.50 | <0.001 | -640.77 | -2082.67 – 801.13 | 0.377 | ||||||
| percent male | -574.15 | -1409.17 – 260.87 | 0.174 | -432.05 | -966.25 – 102.15 | 0.111 | |||||||||
| percentage high education | -5.31 | -9.55 – -1.06 | 0.015 | -131.53 | -249.43 – -13.64 | 0.029 | |||||||||
| Observations | 62 | 61 | 61 | 59 | 62 | ||||||||||
| R2 / R2 adjusted | 0.274 / 0.249 | 0.135 / 0.105 | 0.809 / 0.780 | 0.935 / 0.930 | 0.341 / 0.227 | ||||||||||